Red squirrel territorial vocalizations deter intrusions by conspecific rivals
Bibliographic record
Abstract
Abstract In many species, territory advertisement is thought to be one of the primary functions of acoustic communication. North American red squirrels are a territorial species in which ‘rattles’ have long been thought to be the principal signal communicating territory ownership. These vocalizations have been assumed to deter intruders, thus reducing energetic costs and the risk of injury associated with direct aggressive interactions. However, this hypothesis has not been directly tested. Here we used a speaker occupation experiment to test whether red squirrel rattles function to deter conspecific rivals. We studied 29 male squirrels and removed each individual from his territory twice in a paired design. During the experimental treatment, we simulated the owner’s presence after its removal by broadcasting the owner’s rattle from a loudspeaker at the centre of the territory once every 7 min. During the control treatment, the territory was left in silence following the temporary removal of the owner. We found that the presence of a speaker replacement reduced the probability of intrusion by 34% and increased the latency to first intrusion by 7%, providing support for the hypothesis that rattles play an active role in reducing intrusion risk. However, intrusions were not completely averted by the speaker replacement, indicating that for some individuals vocalizations alone are not a sufficient deterrent without other cues of the territory owner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".